scholarly journals Improved 3D Stem Mapping Method and Elliptic Hypothesis-Based DBH Estimation from Terrestrial Laser Scanning Data

2020 ◽  
Vol 12 (3) ◽  
pp. 352 ◽  
Author(s):  
WenFang Ye ◽  
Chuang Qian ◽  
Jian Tang ◽  
Hui Liu ◽  
XiaoYun Fan ◽  
...  
Keyword(s):  
High Precision ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Estimation Method ◽  
Terrestrial Laser Scanning ◽  
Growth Direction ◽  
Relative Accuracy ◽  
Experimental Results ◽  
Individual Tree ◽  
The Mean

The detailed structure information under the forest canopy is important for forestry surveying. As a high-precision environmental sensing and measurement method, terrestrial laser scanning (TLS) is widely used in high-precision forestry surveying. In TLS-based forestry surveys, stem-mapping, which is focused on detecting and extracting trunks, is one of the core data processing tasks and the basis for the subsequent calculation of tree attributes; one of the most basic attributes is the diameter at breast height (DBH). This article explores and improves the methods for stem mapping and DBH estimation from TLS data. Firstly, an improved 3D stem mapping algorithm considering the growth direction in random sample consistency (RANSAC) cylinder fitting is proposed to extract and fit the individual tree point cloud section. It constructs the hierarchical optimum cylinder of the trunk and introduces the growth direction into the establishment of the backbone buffer in the next layer. Experimental results show that it can effectively remove most of the branches and reduce the interference of the branches to the discrimination of trunks and improve the integrity of stem extraction by about 36%. Secondly, a robust least squares ellipse fitting method based on the elliptic hypothesis is proposed for DBH estimation. Experimental results show that the DBH estimation accuracy of the proposed estimation method is improved compared with other methods. The mean root mean squared error (RMSE) of the proposed estimation method is 1.14 cm, compared with other methods with a mean RMSE of 1.70, 2.03, and 2.14 cm. The mean relative accuracy of the proposed estimation method is 95.2%, compared with other methods with a mean relative accuracy of 92.9%, 91.9%, and 90.9%.

scholarly journals Generating a three-dimensional measuring scene for the forest sector as based on modern geodetic technologies

2021 ◽  
Vol 875 (1) ◽  
pp. 012083
Author(s):  
N Begliarov ◽  
E Mitrofanov ◽  
V Kiseleva
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Method Development ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Accurate Information ◽  
Forest Sector ◽  
Individual Tree ◽  
Cloud Of Points

Abstract Modern geodetic technologies of gathering three-dimensional spatial data incorporate terrestrial laser scanning and aerial photo survey from unmanned aerial vehicles. The combination of these technologies and joint result of survey provide the data of 3D point model and accurate information on trunks and crowns of individual trees. The paper examines the experiment with the application of method of formation of 3D measuring scene in the form of dense cloud of points combining the results of terrestrial laser scanning and materials of photogrammetric processing of UAV-provided data. The method eliminates basic shortcomings of each technology, enhances their advantages, and opens the way to the compilation of more representative 3D measuring scenes. A specific advantage of the method is the outcropping of detailed information on the form, size and condition of individual tree crowns. This option finds a practical application in landscape evaluation and design, remote measuring of trunk parameters excluding the felling of model trees for the compilation of regional timber account tables. The closest perspectives of method development are related to increasing the accuracy of combined survey by specifying flight missions and working with the light regime under forest canopy.

scholarly journals A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data

2019 ◽  
Vol 11 (2) ◽  
pp. 211 ◽  
Author(s):  
Wuming Zhang ◽  
Peng Wan ◽  
Tiejun Wang ◽  
Shangshu Cai ◽  
Yiming Chen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Natural Forests ◽  
Individual Tree ◽  
Urban Scenes ◽  
Novel Approach ◽  
Wide Range ◽  
Standing Trees ◽  
The Mean ◽  
Point Cloud Classification

Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.

scholarly journals Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ting Yun ◽  
Weizheng Li ◽  
Yuan Sun ◽  
Lianfeng Xue
Keyword(s):  
Laser Scanning ◽  
Estimation Method ◽  
Terrestrial Laser Scanning ◽  
Tangent Plane ◽  
Transfer Model ◽  
Normal Vector ◽  
Plant Canopy ◽  
Hemispherical Photography ◽  
Area Index ◽  
Cloud Data

In order to retrieve gap fraction, leaf inclination angle, and leaf area index (LAI) of subtropical forestry canopy, here we acquired forestry detailed information by means of hemispherical photography, terrestrial laser scanning, and LAI-2200 plant canopy analyzer. Meanwhile, we presented a series of image processing and computer graphics algorithms that include image and point cloud data (PCD) segmentation methods for branch and leaf classification and PCD features, such as normal vector, tangent plane extraction, and hemispherical projection method for PCD coordinate transformation. In addition, various forestry mathematical models were proposed to deduce forestry canopy indexes based on the radiation transfer model of Beer-Lambert law. Through the comparison of the experimental results on many plot samples, the terrestrial laser scanner- (TLS-) based index estimation method obtains results similar to digital hemispherical photograph (HP) and LAI-2200 plant canopy analyzer taken of the same stands and used for validation. It indicates that the TLS-based algorithm is able to capture the variability in LAI of forest stands with a range of densities, and there is a high chance to enhance TLS as a calibration tool for other devices.

A Study on the Robustness of Pitch-Range Estimation from Brief Speech Segments

2020 ◽  
Vol 30 (01) ◽  
pp. 2050003
Author(s):  
Wenjie Peng ◽  
Kaiqi Fu ◽  
Wei Zhang ◽  
Yanlu Xie ◽  
Jinsong Zhang
Keyword(s):  
Speaker Recognition ◽  
Native Speakers ◽  
Estimation Method ◽  
Experimental Results ◽  
Percentage Error ◽  
Range Estimation ◽  
Pitch Range ◽  
Speech Segment ◽  
The Mean ◽  
Speech Segments

Pitch-range estimation from brief speech segments could bring benefits to many tasks like automatic speech recognition and speaker recognition. To estimate pitch range, previous studies have proposed to utilize deep-learning-based models with spectrum information as input. They demonstrated that such method works and could still achieve reliable estimation results when the speech segment is as brief as 300 ms. In this study, we evaluated the robustness of this method. We take the following scenarios into account: (1) a large number of training speakers; (2) different language backgrounds; and (3) monosyllabic utterances with different tones. Experimental results showed that: (1) The use of a large number of training speakers improved the estimation accuracies. (2) The mean absolute percentage error (MAPE) rate evaluated on the L2 speakers is similar to that on the native speakers. (3) Different tonal information will affect the LSTM-based model, but this influence is limited compared to the baseline method which calculates pitch-range targets from the distribution of [Formula: see text]0 values. These experimental results verified the efficiency of the LSTM-based pitch-range estimation method.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

2020 ◽  
Author(s):  
Cornelis Stal ◽  
Jeffrey Verbeurgt ◽  
Lars De Sloover ◽  
Alain De Wulf
Keyword(s):  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Terrestrial Laser Scanning ◽  
Accurate Estimation ◽  
Acquisition Time ◽  
Data Sets ◽  
Alternative Technique ◽  
Estimation Model ◽  
Individual Tree ◽  
Airborne Laser

Abstract Sustainable forest management heavily relies on the accurate estimation of tree parameters. Among others, the diameter at breast height (DBH) is important for extracting the volume and mass of an individual tree. For systematically estimating the volume of entire plots, airborne laser scanning (ALS) data are used. The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans (STLS) of sample plots. Although reliable, this method is time-consuming, which greatly hampers its use. Here, a handheld mobile terrestrial laser scanning (HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH. Different data acquisition techniques were applied at a sample plot, then the resulting parameters were comparatively analysed. The calculated DBH values were comparable to the manual measurements for HMTLS, STLS, and ALS data sets. Given the comparability of the extracted parameters, with a reduced point density of HTMLS compared to STLS data, and the reasonable increase of performance, with a reduction of acquisition time with a factor of 5 compared to conventional STLS techniques and a factor of 3 compared to manual measurements, HMTLS is considered a useful alternative technique.

ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Nurliyana Izzati Ishak ◽  
Md Afif Abu Bakar ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Abd Wahid Rasib ◽  
Kasturi Devi Kanniah ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Forest Plot ◽  
Individual Tree ◽  
Matching Process ◽  
Branch Biomass ◽  
Individual Trees ◽  
Tree Stem

This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above.  Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively. 

scholarly journals MAESPA: a model to study interactions between water limitation, environmental drivers and vegetation function at tree and stand levels, with an example application to [CO<sub>2</sub>] × drought interactions

2012 ◽  
Vol 5 (4) ◽  
pp. 919-940 ◽  
Author(s):  
R. A. Duursma ◽  
B. E. Medlyn
Keyword(s):  
Forest Canopy ◽  
Spatial Scales ◽  
Root Water Uptake ◽  
Experimental Results ◽  
Environmental Drivers ◽  
Canopy Interception ◽  
Individual Tree ◽  
Water Limitation ◽  
Leaf Physiology ◽  
Potential Applications

Abstract. Process-based models (PBMs) of vegetation function can be used to interpret and integrate experimental results. Water limitation to plant carbon uptake is a highly uncertain process in the context of environmental change, and many experiments have been carried out that study drought limitations to vegetation function at spatial scales from seedlings to entire canopies. What is lacking in the synthesis of these experiments is a quantitative tool incorporating a detailed mechanistic representation of the water balance that can be used to integrate and analyse experimental results at scales of both the whole-plant and the forest canopy. To fill this gap, we developed an individual tree-based model (MAESPA), largely based on combining the well-known MAESTRA and SPA ecosystem models. The model includes a hydraulically-based model of stomatal conductance, root water uptake routines, drainage, infiltration, runoff and canopy interception, as well as detailed radiation interception and leaf physiology routines from the MAESTRA model. The model can be applied both to single plants of arbitrary size and shape, as well as stands of trees. The utility of this model is demonstrated by studying the interaction between elevated [CO2] (eCa) and drought. Based on theory, this interaction is generally expected to be positive, so that plants growing in eCa should be less susceptible to drought. Experimental results, however, are varied. We apply the model to a previously published experiment on droughted cherry, and show that changes in plant parameters due to long-term growth at eCa (acclimation) may strongly affect the outcome of Ca × drought experiments. We discuss potential applications of MAESPA and some of the key uncertainties in process representation.

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